The present invention relates to systems and methods for estimating noise, and more particularly to systems and methods for estimating signal-dependent noise of images.
Noise in images, e.g., photographs, has several causes including the physical design of cameras (heat, quantization, etc), non-linearities in image processing pipelines, and photon noise due to insufficient exposure time. Some of these, e.g. photon noise, depend on the image signal level and therefore it is desirable to estimate the amount of noise of the image as a function of the image signal level.
Many conventional methods provide techniques for estimating signal-independent noise of images. These methods provide only a single value (typically, one standard deviation, a) that is intended to describe the amount of noise in the image. While this is much easier than obtaining a separate estimate for each signal level, the problem is still seriously ill-posed. Given only the observed image I, the goal is to estimate the latent image O and signal-independent noise P so thatI(x,y)=O(x,y)+P(x,y)  eq. (1)
Typically, it is assumed that P follows Gaussian distribution with zero mean and σ a standard deviation, and the above problem then simplifies toI(x,y)=O(x,y)+Gaussian(0,σ)  eq. (2)
While the characteristics of noise are now known (assuming the noise is signal-independent Gaussian), separation is not possible without additional information. Conventional methods make the assumption that noise is high frequency and the latent image O is not. This allows reasonably good separation in many cases, but the downside is an inevitable loss of high-frequency detail such as textures.
What is needed is an improved technique for estimating noise in images which can avoid the loss of high frequency details of the image.